ENT, head & neck: Overview

Abstract
Purpose: Despite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools.

Methods: Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar.

Results: Significant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability.

Conclusions: Potential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial.

Abstract
Importance: A core component of delivering care of head and neck diseases is an adequate workforce. The World Health Organization report, Multi-Country Assessment of National Capacity to Provide Hearing Care, captured primary workforce estimates from 68 member states in 2012, noting that response rates were a limitation and that updated more comprehensive data are needed.

Objective: To establish comprehensive workforce metrics for global otolaryngology-head and neck surgery (OHNS) with updated data from more countries/territories.

Design, setting, and participants: A cross-sectional electronic survey characterizing the OHNS workforce was disseminated from February 10 to June 22, 2022, to professional society leaders, medical licensing boards, public health officials, and practicing OHNS clinicians.

Main outcome: The OHNS workforce per capita, stratified by income and region.

Results: Responses were collected from 121 of 195 countries/territories (62%). Survey responses specifically reported on OHNS workforce from 114 countries/territories representing 84% of the world's population. The global OHNS clinician density was 2.19 (range, 0-61.7) OHNS clinicians per 100 000 population. The OHNS clinician density varied by World Bank income group with higher-income countries associated with a higher density of clinicians. Regionally, Europe had the highest clinician density (5.70 clinicians per 100 000 population) whereas Africa (0.18 clinicians per 100 000 population) and Southeast Asia (1.12 clinicians per 100 000 population) had the lowest. The OHNS clinicians deliver most of the surgical management of ear diseases and hearing care, rhinologic and sinus diseases, laryngeal disorders, and upper aerodigestive mucosal cancer globally.

Conclusion and relevance: This cross-sectional survey study provides a comprehensive assessment of the global OHNS workforce. These results can guide focused investment in training and policy development to address disparities in the availability of OHNS clinicians.